'''simple docstring'''




import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__UpperCAmelCase					=logging.getLogger(__name__)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	int				="token-classification"






     def __init__(					self						:						List[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         if type(a       ) == dict:
             __lowerCamelCase            =    Namespace(**a       )
         __lowerCamelCase            =    import_module('''tasks'''       )
         try:
             __lowerCamelCase            =    getattr(a    ,							hparams.task_type       )
             __lowerCamelCase            =    token_classification_task_clazz()
         except AttributeError:
             raise ValueError(
                 f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                 f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""       )
         __lowerCamelCase            =    self.token_classification_task.get_labels(hparams.labels       )
         __lowerCamelCase            =    CrossEntropyLoss().ignore_index
         super().__init__(a    ,							len(self.labels       )    ,							self.mode       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							**a						:						Union[str, Any]       ):





         """simple docstring"""



         return self.model(**a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]    ,							a						:						Optional[int]    ,							a						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
             __lowerCamelCase            =    (
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids

         __lowerCamelCase            =    self(**a       )
         __lowerCamelCase            =    outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase            =    self.hparams
         for mode in ["train", "dev", "test"]:
             __lowerCamelCase            =    self._feature_file(a       )
             if os.path.exists(a       ) and not args.overwrite_cache:
                 logger.info('''Loading features from cached file %s'''    ,							a       )
                 __lowerCamelCase            =    torch.load(a       )
             else:
                 logger.info('''Creating features from dataset file at %s'''    ,							args.data_dir       )
                 __lowerCamelCase            =    self.token_classification_task.read_examples_from_file(args.data_dir    ,							a       )
                 __lowerCamelCase            =    self.token_classification_task.convert_examples_to_features(
                     a    ,							self.labels    ,							args.max_seq_length    ,							self.tokenizer    ,							cls_token_at_end=bool(self.config.model_type in ['''xlnet''']       )    ,							cls_token=self.tokenizer.cls_token    ,							cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0    ,							sep_token=self.tokenizer.sep_token    ,							sep_token_extra=a    ,							pad_on_left=bool(self.config.model_type in ['''xlnet''']       )    ,							pad_token=self.tokenizer.pad_token_id    ,							pad_token_segment_id=self.tokenizer.pad_token_type_id    ,							pad_token_label_id=self.pad_token_label_id    ,							)
                 logger.info('''Saving features into cached file %s'''    ,							a       )
                 torch.save(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						int    ,							a						:						int    ,							a						:						bool = False       ):





         """simple docstring"""



         __lowerCamelCase            =    self._feature_file(a       )
         logger.info('''Loading features from cached file %s'''    ,							a       )
         __lowerCamelCase            =    torch.load(a       )
         __lowerCamelCase            =    torch.tensor([f.input_ids for f in features]    ,							dtype=torch.long       )
         __lowerCamelCase            =    torch.tensor([f.attention_mask for f in features]    ,							dtype=torch.long       )
         if features[0].token_type_ids is not None:
             __lowerCamelCase            =    torch.tensor([f.token_type_ids for f in features]    ,							dtype=torch.long       )
         else:
             __lowerCamelCase            =    torch.tensor([0 for f in features]    ,							dtype=torch.long       )
             # HACK(we will not use this anymore soon)
         __lowerCamelCase            =    torch.tensor([f.label_ids for f in features]    ,							dtype=torch.long       )
         return DataLoader(
             TensorDataset(a    ,							a    ,							a    ,							a       )    ,							batch_size=a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						Any    ,							a						:						Optional[int]       ):





         """simple docstring"""



         """Compute validation""" ""
         __lowerCamelCase            =    {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
         if self.config.model_type != "distilbert":
             __lowerCamelCase            =    (
                 batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
             )  # XLM and RoBERTa don"t use token_type_ids
         __lowerCamelCase            =    self(**a       )
         __lowerCamelCase      ,    __lowerCamelCase            =    outputs[:2]
         __lowerCamelCase            =    logits.detach().cpu().numpy()
         __lowerCamelCase            =    inputs['''labels'''].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    torch.stack([x['''val_loss'''] for x in outputs]       ).mean()
         __lowerCamelCase            =    np.concatenate([x['''pred'''] for x in outputs]    ,							axis=0       )
         __lowerCamelCase            =    np.argmax(a    ,							axis=2       )
         __lowerCamelCase            =    np.concatenate([x['''target'''] for x in outputs]    ,							axis=0       )

         __lowerCamelCase            =    dict(enumerate(self.labels       )       )
         __lowerCamelCase            =    [[] for _ in range(out_label_ids.shape[0]       )]
         __lowerCamelCase            =    [[] for _ in range(out_label_ids.shape[0]       )]

         for i in range(out_label_ids.shape[0]       ):
             for j in range(out_label_ids.shape[1]       ):
                 if out_label_ids[i, j] != self.pad_token_label_id:
                     out_label_list[i].append(label_map[out_label_ids[i][j]]       )
                     preds_list[i].append(label_map[preds[i][j]]       )

         __lowerCamelCase            =    {
             '''val_loss''': val_loss_mean,
             '''accuracy_score''': accuracy_score(a    ,							a       ),
             '''precision''': precision_score(a    ,							a       ),
             '''recall''': recall_score(a    ,							a       ),
             '''f1''': fa_score(a    ,							a       ),
         }

         __lowerCamelCase            =    dict(results.items()       )
         __lowerCamelCase            =    results
         return ret, preds_list, out_label_list






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._eval_end(a       )
         __lowerCamelCase            =    ret['''log''']
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    self._eval_end(a       )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         __lowerCamelCase            =    ret['''log''']
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






     @staticmethod
     def   SCREAMING_SNAKE_CASE__  (					a						:						Dict    ,							a						:						int       ):





         """simple docstring"""



         BaseTransformer.add_model_specific_args(a    ,							a       )
         parser.add_argument(
             '''--task_type'''    ,							default='''NER'''    ,							type=a    ,							help='''Task type to fine tune in training (e.g. NER, POS, etc)'''       )
         parser.add_argument(
             '''--max_seq_length'''    ,							default=1_28    ,							type=a    ,							help=(
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated, sequences shorter will be padded.'''
             )    ,							)

         parser.add_argument(
             '''--labels'''    ,							default=''''''    ,							type=a    ,							help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''    ,							)
         parser.add_argument(
             '''--gpus'''    ,							default=0    ,							type=a    ,							help='''The number of GPUs allocated for this, it is by default 0 meaning none'''    ,							)

         parser.add_argument(
             '''--overwrite_cache'''    ,							action='''store_true'''    ,							help='''Overwrite the cached training and evaluation sets'''       )

         return parser


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  __UpperCAmelCase					=NERTransformer.add_model_specific_args(parser, os.getcwd())
  __UpperCAmelCase					=parser.parse_args()
  __UpperCAmelCase					=NERTransformer(args)
  __UpperCAmelCase					=generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    __UpperCAmelCase					=sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True))
    __UpperCAmelCase					=model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)

'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
